Sodium methylparaben is a common food additive, and long-period or excessive ingestion will do harm to the human body. An FS920 fluorescence spectrometer is used to detect sodium methylparaben in orange juice and aqueous solutions. The experimental results show that the characteristic peaks of the orange juice and the aqueous solutions have obvious difference. It is inferred that the interference is mainly from the fluorescence characteristics of orange juice. There exists notable difference between the two solutions with certain concentration range of sodium methylparaben, and the relationship between the relative fluorescence intensity and the sodium methylparaben concentration is complex. Therefore, in order to accurately detect the content of sodium methylparaben in orange juice, fluorescence spectroscopy and least squares support vector machine are combined to establish a model to detect sodium methylparaben in orange juice, and the regularization parameter and the kernel function are obtained with the improved particle swarm optimization algorithm. Compared with ordinary back-propagating (BP) neural network and least squares support vector machine based on particle swarm optimization, the model proposed has optimal performance, the average recovery rate is 97.05%, the average relative error is 2.71%, the root mean square error is about 3.04%, and the correlation coefficient between the model output and the real value is about 0.9999. This method can be used for accurate determination of sodium methylparaben in orange juice.